Talks and Poster Presentations (with Proceedings-Entry):
G. Wang, R. Hasani, Z. Yungang, R. Grosu:
"A Novel Bayesian Network-Based Fault Prognostic Method for Semiconductor Manufacturing Process";
Talk: 2017 Annual IEEE Industrial Electronics Society´s 18th International Conference on Industrial Technology (ICIT 2017),
Toronto, ON, Canada;
- 2017-03-25; in: "Proceedings of the 2017 Annual IEEE Industrial Electronics Society´s 18th International Conference on Industrial Technology (ICIT 2017)",
Fault prognostic in various levels of production of semiconductor chips is considered to be a great challenge. To reduce yield loss during the manufacturing process, tool abnormalities should be detected as early as possible during process monitoring. In this paper, we propose a novel fault prognostic method based on Bayesian networks. The network is designed such that it can process both discrete and continuous variables, to represent the correlations between critical deviations and to process quality control data based on divide-and-conquer strategy. Such a network enables us to perform high-precision multi-step prognostic on the status of the fabrication process given the current state of the sensory info. Additionally, we introduce a layer-wise approach for efficient learning of the Bayesian-network parameters. We evaluate the accuracy of our prognostic model on a wafer fabrication dataset where our model performs precise next-step fault prognostic by using the control sensory data.
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Created from the Publication Database of the Vienna University of Technology.